Finally we compare our temporally aware label cleaning method to the commonly used encoder/decoder multi-sage Temporal Convolution Network (ms-TCN) [1]}. Table REF  shows temporally aware label cleaning out performs a kinetics pretrained ms-TCN (utilising the same feature extraction model), while being able to operate in a near real time manner. We posit this is due to the fact that actions within our dataset are typically much shorter in nature than many other action segmentation datasets and do not necessarily occur in a fixed order, thus removing the advantage of extended temporal reasoning provided by an encoder/decoder method. We further show that features extracted from our assembly dataset trained models can be used to as base features to train ms-TCN models, providing significant improvements over the kinetics feature extraction (F\(_1\) @0.5 51.2 \(\rightarrow \)  78.4) while reducing training time by \(\sim \) 65% ( 200\(\rightarrow \) 70 epochs) showing that significant improvements can be obtained for other encoder/decoder methods via the use of in domain pretraining via the methods outlined in section REF .
